1. Field of the Invention
The present invention generally relates to a novel method for analyzing the Electrocardiogram (ECG) and other physiologic signals of Ventricular Fibrillation (VF) in order to identify and capitalize on the optimum physiologic moments when resuscitation is most likely and also to guide therapy by making therapeutic recommendations as well as predicting rearrest. More particularly, the invention is an integrative model that performs real-time, short-term analysis of ECG through machine learning techniques.
2. Background Description
Sudden cardiac death is a significant public health concern and a leading cause of death in many parts of the world. In the United States cardiac arrest claims greater than 300,000 lives annually. Survival rates for out-of-hospital cardiac arrest remain dismal (cf. G. Nichol, E. Thomas, C. W. Callaway, et al., “Regional variation in out-of-hospital cardiac arrest incidence and outcome”, J Am Med Assoc 2008; 300:1423-1431). Ventricular Fibrillation (VF) is the initially encountered arrhythmia in 20-30% of cardiac arrest cases (cf. V. M. Nadkarni, G. L. Larkin, M. A. Peberdy, S. M. Carey, W. Kaye, M. E. Mancini, G. Nichol, T. Lane-Truitt, J. Potts, J. P. Ornato, and R. A. Berg. “First documented rhythm and clinical outcome from in-hospital cardiac arrest among children and adults”, J Am Med Assoc. 2006; 295:50-57). Multiple reentrant circuits contribute to the VF waveform causing its pathophysiology to be extremely dynamic. A victim's chances of survival worsen by 10% for every minute of VF that remains untreated (cf. T. D. Valenzuela, D. J. Roe, S. Cretin, D. W. Spaite, and M. P. Larsen, “Estimating effectiveness of cardiac arrest interventions: a logistic regression survival model”, Circulation. 1997; 96: 3308-3313). Defibrillation is a procedure that delivers an electrical current that depolarizes a critical mass of the myocardium simultaneously. Defibrillation increases the possibility of the sino-atrial node regaining control of the rhythm. Coronary artery perfusion provided by cardio-pulmonary resuscitation (CPR) prior to defibrillation has been shown to improve chances for return of spontaneous circulation (ROSC). As victims enter the CPR phase of cardiac arrest, predicting defibrillation success may become paramount to prevent unnecessary interruptions to CPR (cf. M. L. Weisfeldt and L. B. Becker, “Resuscitation after cardiac arrest: a 3-phase time-sensitive model”, J Am Med Assoc. 2002; 288 (23)3008-13). Repetitive unsuccessful shocks can reduce chest compression time and can cause injury to cardiac tissue, impacting heart function upon survival. Even worse, unsuccessful shocks can cause VF to deteriorate into asystole or pulseless electrical activity (PEA), which are more difficult to resuscitate (cf. H. Strohmenger, “Predicting Defibrillation Success”, Cardiopulmonary Resuscitation, 2008; 14:311-316).
The effect of acute ischemia on tissue excitability induces conversion of VF from type-1 coarse VF to type-2 smooth VF (cf. A. V. Zaitsev, O. Berenfeld, S. F. Mironov, J. Jalife, and A. M. Pertsov, “Distribution of excitation frequencies on the epicardial and endocardial surfaces of fibrillating ventricular wall of the sheep heart”, Circ Res., 2000; 86:408-417). Type 1 VF has now been correlated with the multiple-wavelet theory, while type-2 has been shown to be driven by a mother rotor (cf. J. N. Weiss, Z. Qu, P. S. Chen, S. F. Lin, H. S. Karagueuzian, H. Hayashi, A. Garfinkel, and A. Karma, “The Dynamics of Cardiac Fibrillation”, Circulation, 2005; 112:1232-1240). This conversion partially conforms to rapidly attenuating chances of survival with increasing VF duration (cf. J. Eilevstjonn, J. Kramer-Johansen, and K. Sunde, “Shock outcome is related to prior rhythm and duration of ventricular fibrillation”, Resuscitation, 2007, 75: 60-6), and can be quantified by any measure that can account for both, a decrease in amplitude and a shift in spectral composition of the signal. Fourier Transform (FT) based measures (cf. G. Ristagno, A. Gullo, G. Berlot, U. Lucangelo, F. Geheb, and J. Bisera, “Prediction of successful defibrillation in human victims of out-of-hospital cardiac arrest: a retrospective electrocardiographic analysis”, Anaesth Intensive Care 2008; 36: 46-50) assume a linear, deterministic basis for the signals, and prove to be impracticable. Other methods (cf. J. N. Watson, N. Uchaipichat, P. S. Addison, G. R. Clegg, C. E. Robertson, T. Eftestol T, and P. A. Steen, “Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods”, Resuscitation 63: 269-275, 2004, and A. Neurauter, T. Eftestøl, and H-U. Strohmenger. “Prediction of countershock success using single features from multiple ventricular fibrillation frequency bands and feature combinations using neural networks”, Resuscitation 73, 253-263, 2007), with somewhat more feasible definitions of post-shock success, have focused on creating predictive models based on the real Discrete Wavelet Transform (DWT). While wavelet decomposition has proven to be more effective, clinical transition of such approaches has been precluded due to low specificities.
Gundersen and colleagues (cf. K. Gundersen et al, “Identifying approaches to improve the accuracy of shock outcome prediction for out-of-hospital cardiac arrest”, Resuscitation. Volume 76, Issue 2, February 2008, Pages 279-284) have shown that predictive features of the VF waveform suffer from random effects, with p-values less than 10-3. This was proved with a mixed effects logistic regression model. Random effect-sizes, calculated as standard deviation of the “random” term in the model, varied from 73% to 189% of the feature effect-sizes. Thus an additional objective of our work aims at countering the variance due to such effects. We hypothesized that other physiologic signals obtained during CPR, such as end-tidal carbon dioxide (PetCO2), can help build a more “complete” model.